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Clinical and Experimental Optometry Volume 89, 2006 - Issue 5
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Original Papers

An automated microaneurysm detector as a tool for identification of diabetic retinopathy in rural optometric practice

Herbert J JelinekSchool of Community Health, Charles Sturt University, Albury, AustraliaCorrespondence[email protected]
, PhD,
Michael J CreeDepartment of Physics and Electronic Engineering, University of Waikato, Hamilton, New Zealand
, PhD,
David WorsleyEye Clinic, Waikato Hospital, Hamilton, New Zealand
, MBChB FRANZCO,
Alan LuckieAlbury Eye Clinic, Albury, Australia
, MBChB FRANZCO &
Peter NixonGraham Hill Optometrist and Associates, Shepparton, Australia
E‐mail: [email protected]
, BSc(Hons) BO
Pages 299-305 | Received 09 Dec 2005, Accepted 23 Apr 2006, Published online: 15 Apr 2021

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Namita Sengar & Malay Kishore Dutta. (2018) Automated method for hierarchal detection and grading of diabetic retinopathy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 6:6, pages 649-659.
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Articles from other publishers (32)

David Alonso-Moro, Guillermo Hernández, Jesús Martín-Gonzalez & FJ. García-Criado. 2023. Distributed Computing and Artificial Intelligence, 19th International Conference. Distributed Computing and Artificial Intelligence, 19th International Conference 65 75 .
Abdelali Elmoufidi & Hind Ammoun. (2022) Diabetic Retinopathy Prevention Using EfficientNetB3 Architecture and Fundus Photography. SN Computer Science 4:1.
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Cheng Wan, Yingsi Chen, Han Li, Bo Zheng, Nan Chen, Weihua Yang, Chenghu Wang & Yan Li. (2021) EAD-Net: A Novel Lesion Segmentation Method in Diabetic Retinopathy Using Neural Networks. Disease Markers 2021, pages 1-13.
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Nicola Quinn, Laima Brazionis, Benjamin Zhu, Chris Ryan, Rossella D’Aloisio, Hongying Lilian Tang, Tunde Peto & Alicia Jenkins. (2021) Facilitating diabetic retinopathy screening using automated retinal image analysis in underresourced settings. Diabetic Medicine 38:9.
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Israa Odeh, Mouhammd Alkasassbeh & Mohammad Alauthman. (2021) Diabetic Retinopathy Detection using Ensemble Machine Learning. Diabetic Retinopathy Detection using Ensemble Machine Learning.
Christy Lyona, Joyce Menezes, Tushar Shinde, Mayura Gavhane, Ragini M. Rohatgi & Satishkumar Chavan. (2020) Classification of retinal images in stages of diabetic retinopathy using deep learning. Classification of retinal images in stages of diabetic retinopathy using deep learning.
Shilpa Joshi & PT Karule. (2019) Mathematical morphology for microaneurysm detection in fundus images. European Journal of Ophthalmology 30:5, pages 1135-1142.
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Pedro Bibiloni, Manuel González-Hidalgo & Sebastia Massanet. (2018) A real-time fuzzy morphological algorithm for retinal vessel segmentation. Journal of Real-Time Image Processing 16:6, pages 2337-2350.
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Aakanksha Mahajan, Sushil Kumar & Rohit Bansal. 2019. Proceedings of 2nd International Conference on Communication, Computing and Networking. Proceedings of 2nd International Conference on Communication, Computing and Networking 393 406 .
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Carmen Valverde, Maria Garcia, Roberto Hornero & MariaI Lopez-Galvez. (2016) Automated detection of diabetic retinopathy in retinal images. Indian Journal of Ophthalmology 64:1, pages 26.
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Michael David Abràmoff & Meindert Niemeijer. 2015. Teleophthalmology in Preventive Medicine. Teleophthalmology in Preventive Medicine 41 50 .
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Ramon Pires, Herbert F. Jelinek, Jacques Wainer, Eduardo Valle & Anderson Rocha. (2014) Advancing Bag-of-Visual-Words Representations for Lesion Classification in Retinal Images. PLoS ONE 9:6, pages e96814.
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Bálint Antal & András Hajdu. (2014) An ensemble-based system for automatic screening of diabetic retinopathy. Knowledge-Based Systems 60, pages 20-27.
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Sarni Suhaila Rahim, Vasile Palade, James Shuttleworth & Chrisina Jayne. 2014. Engineering Applications of Neural Networks. Engineering Applications of Neural Networks 113 122 .
Muthu Rama Krishnan Mookiah, U. Rajendra Acharya, Chua Kuang Chua, Choo Min Lim, E.Y.K. Ng & Augustinus Laude. (2013) Computer-aided diagnosis of diabetic retinopathy: A review. Computers in Biology and Medicine 43:12, pages 2136-2155.
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M.R.K. Mookiah, U. Rajendra Acharya, Roshan Joy Martis, Chua Kuang Chua, C.M. Lim, E.Y.K. Ng & Augustinus Laude. (2013) Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: A hybrid feature extraction approach. Knowledge-Based Systems 39, pages 9-22.
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Niladri Sekhar Datta, Himadri Sekhar Dutta, Mallika De & Saurajeet Mondal. (2013) An Effective Approach: Image Quality Enhancement for Microaneurysms Detection of Non-dilated Retinal Fundus Image. Procedia Technology 10, pages 731-737.
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A. Rocha, T. Carvalho, H. F. Jelinek, S. Goldenstein & J. Wainer. (2012) Points of Interest and Visual Dictionaries for Automatic Retinal Lesion Detection. IEEE Transactions on Biomedical Engineering 59:8, pages 2244-2253.
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B. Antal & A. Hajdu. (2012) An Ensemble-Based System for Microaneurysm Detection and Diabetic Retinopathy Grading. IEEE Transactions on Biomedical Engineering 59:6, pages 1720-1726.
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Oliver Faust, Rajendra Acharya U.E. Y. K. Ng, Kwan-Hoong Ng & Jasjit S. Suri. (2010) Algorithms for the Automated Detection of Diabetic Retinopathy Using Digital Fundus Images: A Review. Journal of Medical Systems 36:1, pages 145-157.
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Michael J. Cree, John P. Godbaz, Robert Larkins, W. Howell Round, Lee Streeter, Adrian A. Dorrington, Rainer Kunnemeyer, Andrew D. Payne & David Worsley. (2010) Computer vision and image processing at the University of Waikato. Computer vision and image processing at the University of Waikato.
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C. Agurto, V. Murray, E. Barriga, S. Murillo, M. Pattichis, H. Davis, S. Russell, M. Abramoff & P. Soliz. (2010) Multiscale AM-FM Methods for Diabetic Retinopathy Lesion Detection. IEEE Transactions on Medical Imaging 29:2, pages 502-512.
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Herbert Jelinek & Michael CreeMichael Cree. 2009. Automated Image Detection of Retinal Pathology. Automated Image Detection of Retinal Pathology.
Herbert Jelinek & Michael CreeDavid Worsley & David Simmons. 2009. Automated Image Detection of Retinal Pathology. Automated Image Detection of Retinal Pathology.
Herbert Jelinek & Michael CreeFred Reinholz, Ian Constable & Kanagasingam Yogesan. 2009. Automated Image Detection of Retinal Pathology. Automated Image Detection of Retinal Pathology.
D. J. Cornforth, H. F. Jelinek, M. J. Cree, J. J. G. Leandro, J. V. B. Soares & R. M. CesarJr.Jr.. 2009. Intelligent and Evolutionary Systems. Intelligent and Evolutionary Systems 171 182 .
Manal BouhaimedRobbie GibbinsDavid Owens. (2008) Automated Detection of Diabetic Retinopathy: Results of a Screening Study. Diabetes Technology & Therapeutics 10:2, pages 142-148.
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Wong Li Yun, U. Rajendra Acharya, Y.V. Venkatesh, Caroline Chee, Lim Choo Min & E.Y.K. Ng. (2008) Identification of different stages of diabetic retinopathy using retinal optical images. Information Sciences 178:1, pages 106-121.
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. (2007) Current World Literature. Current Opinion in Ophthalmology 18:6, pages 515-523.
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